






'''simple docstring'''





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import AlignProcessor, EfficientNetImageProcessor


@require_vision
class  snake_case__      (					unittest.TestCase):




					def 		A      (  self  :		Dict      )    ->							Dict:
								UpperCAmelCase_      :       Tuple          =       tempfile.mkdtemp()

								UpperCAmelCase_      :       Tuple          =       [
								    '''[UNK]''',
								    '''[CLS]''',
								    '''[SEP]''',
								    '''[PAD]''',
								    '''[MASK]''',
								    '''want''',
								    '''##want''',
								    '''##ed''',
								    '''wa''',
								    '''un''',
								    '''runn''',
								    '''##ing''',
								    ''',''',
								    '''low''',
								    '''lowest''',
								]
								UpperCAmelCase_      :       str          =       os.path.join(self.tmpdirname				, VOCAB_FILES_NAMES['''vocab_file''']      )
								with open(self.vocab_file				, '''w'''				, encoding='''utf-8'''      ) as vocab_writer:
											vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]      )      )

								UpperCAmelCase_      :       str          =       {
								    '''do_resize''': True,
								    '''size''': 20,
								    '''do_center_crop''': True,
								    '''crop_size''': 18,
								    '''do_normalize''': True,
								    '''image_mean''': [0.48_145_466, 0.4_578_275, 0.40_821_073],
								    '''image_std''': [0.26_862_954, 0.26_130_258, 0.27_577_711],
								}
								UpperCAmelCase_      :       List[Any]          =       os.path.join(self.tmpdirname				, _A      )
								with open(self.image_processor_file				, '''w'''				, encoding='''utf-8'''      ) as fp:
											json.dump(_A				, _A      )




					def 		A      (  self  :		Union[str, Any]				, **_A  :		List[Any]      )    ->							Any:
								return BertTokenizer.from_pretrained(self.tmpdirname				, **_A      )




					def 		A      (  self  :		List[str]				, **_A  :		List[Any]      )    ->							int:
								return BertTokenizerFast.from_pretrained(self.tmpdirname				, **_A      )




					def 		A      (  self  :		Dict				, **_A  :		List[str]      )    ->							Any:
								return EfficientNetImageProcessor.from_pretrained(self.tmpdirname				, **_A      )




					def 		A      (  self  :		Tuple      )    ->							Union[str, Any]:
								shutil.rmtree(self.tmpdirname      )




					def 		A      (  self  :		Tuple      )    ->							str:
								UpperCAmelCase_      :       Any          =       [np.random.randint(2_55				, size=(3, 30, 4_00)				, dtype=np.uinta      )]
								UpperCAmelCase_      :       str          =       [Image.fromarray(np.moveaxis(_A				, 0				, -1      )      ) for x in image_inputs]
								return image_inputs




					def 		A      (  self  :		List[Any]      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       List[str]          =       self.get_tokenizer()
								UpperCAmelCase_      :       Union[str, Any]          =       self.get_rust_tokenizer()
								UpperCAmelCase_      :       List[str]          =       self.get_image_processor()

								UpperCAmelCase_      :       str          =       AlignProcessor(tokenizer=_A				, image_processor=_A      )
								processor_slow.save_pretrained(self.tmpdirname      )
								UpperCAmelCase_      :       Union[str, Any]          =       AlignProcessor.from_pretrained(self.tmpdirname				, use_fast=_A      )

								UpperCAmelCase_      :       List[Any]          =       AlignProcessor(tokenizer=_A				, image_processor=_A      )
								processor_fast.save_pretrained(self.tmpdirname      )
								UpperCAmelCase_      :       Dict          =       AlignProcessor.from_pretrained(self.tmpdirname      )

								self.assertEqual(processor_slow.tokenizer.get_vocab()				, tokenizer_slow.get_vocab()      )
								self.assertEqual(processor_fast.tokenizer.get_vocab()				, tokenizer_fast.get_vocab()      )
								self.assertEqual(tokenizer_slow.get_vocab()				, tokenizer_fast.get_vocab()      )
								self.assertIsInstance(processor_slow.tokenizer				, _A      )
								self.assertIsInstance(processor_fast.tokenizer				, _A      )

								self.assertEqual(processor_slow.image_processor.to_json_string()				, image_processor.to_json_string()      )
								self.assertEqual(processor_fast.image_processor.to_json_string()				, image_processor.to_json_string()      )
								self.assertIsInstance(processor_slow.image_processor				, _A      )
								self.assertIsInstance(processor_fast.image_processor				, _A      )




					def 		A      (  self  :		Any      )    ->							Any:
								UpperCAmelCase_      :       Any          =       AlignProcessor(tokenizer=self.get_tokenizer()				, image_processor=self.get_image_processor()      )
								processor.save_pretrained(self.tmpdirname      )

								UpperCAmelCase_      :       str          =       self.get_tokenizer(bos_token='''(BOS)'''				, eos_token='''(EOS)'''      )
								UpperCAmelCase_      :       List[str]          =       self.get_image_processor(do_normalize=_A				, padding_value=1.0      )

								UpperCAmelCase_      :       int          =       AlignProcessor.from_pretrained(
								    self.tmpdirname				, bos_token='''(BOS)'''				, eos_token='''(EOS)'''				, do_normalize=_A				, padding_value=1.0      )

								self.assertEqual(processor.tokenizer.get_vocab()				, tokenizer_add_kwargs.get_vocab()      )
								self.assertIsInstance(processor.tokenizer				, _A      )

								self.assertEqual(processor.image_processor.to_json_string()				, image_processor_add_kwargs.to_json_string()      )
								self.assertIsInstance(processor.image_processor				, _A      )




					def 		A      (  self  :		List[str]      )    ->							List[Any]:
								UpperCAmelCase_      :       Optional[int]          =       self.get_image_processor()
								UpperCAmelCase_      :       Optional[Any]          =       self.get_tokenizer()

								UpperCAmelCase_      :       List[str]          =       AlignProcessor(tokenizer=_A				, image_processor=_A      )

								UpperCAmelCase_      :       int          =       self.prepare_image_inputs()

								UpperCAmelCase_      :       Optional[Any]          =       image_processor(_A				, return_tensors='''np'''      )
								UpperCAmelCase_      :       Tuple          =       processor(images=_A				, return_tensors='''np'''      )

								for key in input_image_proc.keys():
											self.assertAlmostEqual(input_image_proc[key].sum()				, input_processor[key].sum()				, delta=1e-2      )




					def 		A      (  self  :		Tuple      )    ->							Any:
								UpperCAmelCase_      :       List[Any]          =       self.get_image_processor()
								UpperCAmelCase_      :       int          =       self.get_tokenizer()

								UpperCAmelCase_      :       List[Any]          =       AlignProcessor(tokenizer=_A				, image_processor=_A      )

								UpperCAmelCase_      :       int          =       '''lower newer'''

								UpperCAmelCase_      :       Any          =       processor(text=_A      )

								UpperCAmelCase_      :       str          =       tokenizer(_A				, padding='''max_length'''				, max_length=64      )

								for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]				, encoded_processor[key]      )




					def 		A      (  self  :		Dict      )    ->							Optional[Any]:
								UpperCAmelCase_      :       str          =       self.get_image_processor()
								UpperCAmelCase_      :       Optional[Any]          =       self.get_tokenizer()

								UpperCAmelCase_      :       Any          =       AlignProcessor(tokenizer=_A				, image_processor=_A      )

								UpperCAmelCase_      :       int          =       '''lower newer'''
								UpperCAmelCase_      :       List[Any]          =       self.prepare_image_inputs()

								UpperCAmelCase_      :       str          =       processor(text=_A				, images=_A      )

								self.assertListEqual(list(inputs.keys()      )				, ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']      )

								# test if it raises when no input is passed
								with pytest.raises(_A      ):
											processor()




					def 		A      (  self  :		List[Any]      )    ->							str:
								UpperCAmelCase_      :       int          =       self.get_image_processor()
								UpperCAmelCase_      :       Any          =       self.get_tokenizer()

								UpperCAmelCase_      :       str          =       AlignProcessor(tokenizer=_A				, image_processor=_A      )

								UpperCAmelCase_      :       str          =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								UpperCAmelCase_      :       int          =       processor.batch_decode(_A      )
								UpperCAmelCase_      :       Dict          =       tokenizer.batch_decode(_A      )

								self.assertListEqual(_A				, _A      )





					def 		A      (  self  :		Union[str, Any]      )    ->							Any:
								UpperCAmelCase_      :       Any          =       self.get_image_processor()
								UpperCAmelCase_      :       List[str]          =       self.get_tokenizer()

								UpperCAmelCase_      :       List[Any]          =       AlignProcessor(tokenizer=_A				, image_processor=_A      )

								UpperCAmelCase_      :       str          =       '''lower newer'''
								UpperCAmelCase_      :       Optional[Any]          =       self.prepare_image_inputs()

								UpperCAmelCase_      :       str          =       processor(text=_A				, images=_A      )

								self.assertListEqual(list(inputs.keys()      )				, processor.model_input_names      )








'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase_					=   {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
      "SwinForImageClassification",
      "SwinForMaskedImageModeling",
      "SwinModel",
      "SwinPreTrainedModel",
      "SwinBackbone",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFSwinForImageClassification",
      "TFSwinForMaskedImageModeling",
      "TFSwinModel",
      "TFSwinPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_swin import (
        SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
        SwinBackbone,
        SwinForImageClassification,
        SwinForMaskedImageModeling,
        SwinModel,
        SwinPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_swin import (
        TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFSwinForImageClassification,
        TFSwinForMaskedImageModeling,
        TFSwinModel,
        TFSwinPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



